"""simple docstring"""



import unittest

from huggingface_hub import hf_hub_download

from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_decord,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
)

from .test_pipelines_common import ANY






@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class 	__lowerCAmelCase					(      unittest.TestCase):




    '''simple docstring'''






    __magic_name__   :			Tuple       		=       MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING





    def 					_UpperCAmelCase		(     self				:	Dict					,				UpperCamelCase__				:	str					,				UpperCamelCase__				:	Any					,				UpperCamelCase__				:	int							):
          A__	:      List[str]      =hf_hub_download(
              repo_id="nateraw/video-demo"					,				filename="archery.mp4"					,				repo_type="dataset"							)
          A__	:      Optional[int]      =VideoClassificationPipeline(model=__snake_case					,				image_processor=__snake_case					,				top_k=2							)
          A__	:      List[Any]      =[
              example_video_filepath,
              """https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4""",
          ]
          return video_classifier, examples





    def 					_UpperCAmelCase		(     self				:	int					,				UpperCamelCase__				:	List[str]					,				UpperCamelCase__				:	List[Any]							):
          for example in examples:
                A__	:      Optional[Any]      =video_classifier(__snake_case							)

                self.assertEqual(
                    __snake_case					,				[
                        {"score": ANY(__snake_case							), "label": ANY(__snake_case							)},
                        {"score": ANY(__snake_case							), "label": ANY(__snake_case							)},
                    ]					,				)





    @require_torch
    def 					_UpperCAmelCase		(     self				:	Any							):
          A__	:      List[str]      ="""hf-internal-testing/tiny-random-VideoMAEForVideoClassification"""
          A__	:      Tuple      =VideoMAEFeatureExtractor(
              size={"shortest_edge": 10}					,				crop_size={"height": 10, "width": 10}							)
          A__	:      Optional[Any]      =pipeline(
              "video-classification"					,				model=__snake_case					,				feature_extractor=__snake_case					,				frame_sampling_rate=4							)

          A__	:      Optional[int]      =hf_hub_download(repo_id="nateraw/video-demo"					,				filename="archery.mp4"					,				repo_type="dataset"							)
          A__	:      Dict      =video_classifier(__snake_case					,				top_k=2							)
          self.assertEqual(
              nested_simplify(__snake_case					,				decimals=4							)					,				[{"score": 0.5199, "label": "LABEL_0"}, {"score": 0.4801, "label": "LABEL_1"}]					,				)

          A__	:      Dict      =video_classifier(
              [
                  video_file_path,
                  video_file_path,
              ]					,				top_k=2					,				)
          self.assertEqual(
              nested_simplify(__snake_case					,				decimals=4							)					,				[
                  [{"score": 0.5199, "label": "LABEL_0"}, {"score": 0.4801, "label": "LABEL_1"}],
                  [{"score": 0.5199, "label": "LABEL_0"}, {"score": 0.4801, "label": "LABEL_1"}],
              ]					,				)





    @require_tf
    def 					_UpperCAmelCase		(     self				:	int							):
          pass









'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_						:  int				  =						{'configuration_wavlm': ['WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WavLMConfig']}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_						:  Tuple				  =						[
				    'WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'WavLMForAudioFrameClassification',
				    'WavLMForCTC',
				    'WavLMForSequenceClassification',
				    'WavLMForXVector',
				    'WavLMModel',
				    'WavLMPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_wavlm import (
								    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    WavLMForAudioFrameClassification,
								    WavLMForCTC,
								    WavLMForSequenceClassification,
								    WavLMForXVector,
								    WavLMModel,
								    WavLMPreTrainedModel,
								)

else:
				import sys

				UpperCAmelCase_						:  Any				  =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




